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Sensing and Imaging in Computer Vision

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 1 April 2026 | Viewed by 773

Special Issue Editor


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Guest Editor
Faculty of Electronics, Telecommunications and Information Technologies, Polytechnic University Timisoara, 300223 Timisoara, Romania
Interests: computer vision; artificial intelligence; image processing

Special Issue Information

Dear Colleagues,

Recent advances in sensing technologies and imaging systems have significantly enhanced the capabilities of computer vision applications across various domains, including robotics, healthcare, environmental monitoring, and smart cities. This Special Issue aims to bring together cutting-edge research that explores novel sensing modalities, image acquisition techniques, and intelligent vision algorithms designed to interpret complex scenes and dynamic environments. Emphasis is placed on both theoretical innovations and practical implementations that push the boundaries of sensing-driven computer vision.

Prof. Dr. Codruta Orniana Ancuti
Guest Editor

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Keywords

  • computer vision
  • imaging sensors
  • multimodal sensing
  • AI in image understanding
  • remote sensing
  • sensor fusion

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Published Papers (2 papers)

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Research

18 pages, 4066 KiB  
Article
Video Segmentation of Wire + Arc Additive Manufacturing (WAAM) Using Visual Large Model
by Shuo Feng, James Wainwright, Chong Wang, Jun Wang, Goncalo Rodrigues Pardal, Jian Qin, Yi Yin, Shakirudeen Lasisi, Jialuo Ding and Stewart Williams
Sensors 2025, 25(14), 4346; https://doi.org/10.3390/s25144346 - 11 Jul 2025
Viewed by 226
Abstract
Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based [...] Read more.
Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based upon this information, an automatic and robust segmentation method for monitoring of videos and images is required. However, video segmentation in WAAM and welding is challenging due to constantly fluctuating arc brightness, which varies with deposition and welding configurations. Additionally, conventional computer vision algorithms based on greyscale value and gradient lack flexibility and robustness in this scenario. Deep learning offers a promising approach to WAAM video segmentation; however, the prohibitive time and cost associated with creating a well-labelled, suitably sized dataset have hindered its widespread adoption. The emergence of large computer vision models, however, has provided new solutions. In this study a semi-automatic annotation tool for WAAM videos was developed based upon the computer vision foundation model SAM and the video object tracking model XMem. The tool can enable annotation of the video frames hundreds of times faster than traditional manual annotation methods, thus making it possible to achieve rapid quantitative analysis of WAAM and welding videos with minimal user intervention. To demonstrate the effectiveness of the tool, three cases are demonstrated: online wire position closed-loop control, droplet transfer behaviour analysis, and assembling a dataset for dedicated deep learning segmentation models. This work provides a broader perspective on how to exploit large models in WAAM and weld deposits. Full article
(This article belongs to the Special Issue Sensing and Imaging in Computer Vision)
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24 pages, 11104 KiB  
Article
HGCS-Det: A Deep Learning-Based Solution for Localizing and Recognizing Household Garbage in Complex Scenarios
by Houkui Zhou, Chang Chen, Zhongyi Xia, Qifeng Ding, Qinqin Liao, Qun Wang, Huimin Yu, Haoji Hu, Guangqun Zhang, Junguo Hu and Tao He
Sensors 2025, 25(12), 3726; https://doi.org/10.3390/s25123726 - 14 Jun 2025
Viewed by 391
Abstract
With the rise of deep learning technology, intelligent garbage detection provides a new idea for garbage classification management. However, due to the interference of complex environments, coupled with the influence of the irregular features of garbage, garbage detection in complex scenarios still faces [...] Read more.
With the rise of deep learning technology, intelligent garbage detection provides a new idea for garbage classification management. However, due to the interference of complex environments, coupled with the influence of the irregular features of garbage, garbage detection in complex scenarios still faces significant challenges. Moreover, some of the existing research suffer from shortcomings in either their precision or real-time performance, particularly when applied to complex garbage detection scenarios. Therefore, this paper proposes a model based on YOLOv8, namely HGCS-Det, for detecting garbage in complex scenarios. The HGCS-Det model is designed as follows: Firstly, the normalization attention module is introduced to calibrate the model’s attention to targets and to suppress the environmental noise interference information. Additionally, to weigh the attention-feature contributions, an Attention Feature Fusion module is employed to complement the attention weights of each channel. Subsequently, an Instance Boundary Reinforcement module is established to capture the fine-grained features of garbage by combining strong gradient information with semantic information. Finally, the Slide Loss function is applied to dynamically weight hard samples arising from the complex detection environments to improve the recognition accuracy of hard samples. With only a slight increase in parameters (3.02M), HGCS-Det achieves a 93.6% mean average precision (mAP) and 86 FPS on the public HGI30 dataset, which is a 3.33% higher mAP value than from YOLOv12, and outperforms the state-of-the-art (SOTA) methods in both efficiency and applicability. Notably, HGCS-Det maintains a lightweight architecture while enhancing the detection accuracy, enabling real-time performance even in resource-constrained environments. These characteristics significantly improve its practical applicability, making the model well suited for deployment in embedded devices and real-world garbage classification systems. This method can serve as a valuable technical reference for the engineering application of garbage classification. Full article
(This article belongs to the Special Issue Sensing and Imaging in Computer Vision)
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